Chapter 1 of 7 · 4 practices · 3 min · 3-question quiz
Problem framing and baselines
Decide what the model predicts, what decision it supports and what it has to beat before you train anything. These practices set the bar every later chapter measures against.
Start: Write down the prediction, the decision it drives and the cost of each kind of mistake4 practices in this chapter
- 1Write down the prediction, the decision it drives and the cost of each kind of mistakeA model is a means to a decision: approve a loan, flag an email, recommend an article. Before choosing an algorithm, write one paragraph that answers: What is predicted, for which unit (a user, a…
- 2Check whether a heuristic already solves the problemRule #1 of Google's Rules of ML is not to be afraid to launch a product without machine learning. A rule such as "flag transactions over a set amount from a new device" can be shipped quickly…
- 3Score a dummy baseline on the same split and metric as your modelscikit-learn's docs describe comparing an estimator against simple rules of thumb as a basic sanity check, and provide
DummyClassifierandDummyRegressorfor it. They ignore the input features… - 4Start with a simple, interpretable model before reaching for deep learningRule #4 says to keep the first model simple and get the infrastructure right, and Rule #14 notes that starting with an interpretable model makes debugging easier. A logistic regression or a gradient…
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